2 papers with code • 1 benchmarks • 1 datasets
In this paper, we introduce robust and synergetic hand-crafted features and a simple but efficient deep feature from a convolutional neural network (CNN) architecture for defocus estimation.
Ranked #2 on Defocus Estimation on CUHK - Blur Detection Dataset
Our method is evaluated on publicly available blur detection and blur estimation datasets and the results show the state-of-the-art performance. In this paper, we propose the first end-to-end convolutional neural network (CNN) architecture, Defocus Map Estimation Network (DMENet), for spatially varying defocus map estimation.
Ranked #1 on Defocus Estimation on CUHK - Blur Detection Dataset